Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

On Designing Collaborative Robotic Systems with Real-Time Operating Systems and Wireless Networks

Robotic devices currently solve many real-world problems but do so primarily on an individual basis. The ability to deploy large quantities of robots to solve problems has not yet been widely embraced partially due to the complex nature of robotics and levels of research investment. Existing research in robot collaboration largely exists in the forms of models and simulations. This work seeks to accelerate the next level of research in this area by providing a low cost, collaborative capable robotic system. This platform can be used as a gateway to transport simulations into physical representations. This research not only presents a completed robot but also provides a guide for fellow researchers to use when custom tailored systems are required. Harnessing the power of robotic collaboration will inspire a new generation of problem solving and eventually produce products that could even save lives. This work demonstrates the principles and technologies needed to design robots in general and for use in collaborative environments

Development of a miniature robot for swarm robotic application

Biological swarm is a fascinating behavior of nature that has been successfully applied to solve human problem especially for robotics application. The high economical cost and large area required to execute swarm robotics scenarios does not permit experimentation with real robot. Model and simulation of the mass number of these robots are extremely complex and often inaccurate. This paper describes the design decision and presents the development of an autonomous miniature mobile-robot (AMiR) for swarm robotics research and education. The large number of robot in these systems allows designing an individual AMiR unit with simple perception and mobile abilities. Hence a large number of robots can be easily and economically feasible to be replicated. AMiR has been designed as a complete platform with supporting software development tools for robotics education and researches in the Department of Computer and Communication Systems Engineering, UPM. The experimental results demonstrate the feasibility of using this robot to implement swarm robotic applications

A compact atomic magnetometer for cubesats

By shining a precisely tuned laser through an atomic vapor, we can determine local mag- netic field strength in scalar form and in a way that is not affected by temperature changes. This technology has been used in space many times before on missions flown by NASA and ESA, such as SWARM, Øersted, and CHAMP to calibrate accompanying vector mag- netometers which are subject to offsets caused by temperature changes. The device we constructed is a small, low-cost application of this scientific principle and opens up new areas of scientific possibility for cubesats and the ability to define geomagnetic field struc- tures on a small (<10km) scale as part of the ANDESITE cubesat mission being developed at Boston University. Previously, magnetic sensors in orbit have been flown individually on a single spacecraft or in very small groups such as the International Sun-Earth Exporers (ISEE) and SWARM which each used three separate spacecraft. This method of analyzing the geomagnetic field cannot provide a spatial or time resolution smaller than that of the separation between magnetic field readings. This project has focused on producing a tabletop demonstra- tion of a compact sensor head which could enable measurements on unprecedented small scales. Toward this end we have accomplished the construction and preliminary testing of a compact sensor head which contains all necessary elements to function as a scalar atomic magnetometer

초미세 회로 설계를 위한 인터커넥트의 타이밍 분석 및 디자인 룰 위반 예측

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 김태환.타이밍 분석 및 디자인 룰 위반 제거는 반도체 칩 제조를 위한 마스크 제작 전에 완료되어야 할 필수 과정이다. 그러나 트랜지스터와 인터커넥트의 변이가 증가하고 있고 디자인 룰 역시 복잡해지고 있기 때문에 타이밍 분석 및 디자인 룰 위반 제거는 초미세 회로에서 더 어려워지고 있다. 본 논문에서는 초미세 설계를 위한 두가지 문제인 타이밍 분석과 디자인 룰 위반에 대해 다룬다. 첫번째로 공정 코너에서 타이밍 분석은 실리콘으로 제작된 회로의 성능을 정확히 예측하지 못한다. 그 이유는 공정 코너에서 가장 느린 타이밍 경로가 모든 공정 조건에서도 가장 느린 것은 아니기 때문이다. 게다가 칩 내의 임계 경로에서 인터커넥트에 의한 지연 시간이 전체 지연 시간에서의 영향이 증가하고 있고, 10나노 이하 공정에서는 20%를 초과하고 있다. 즉, 실리콘으로 제작된 회로의 성능을 정확히 예측하기 위해서는 대표 회로가 트랜지스터의 변이 뿐만아니라 인터커넥트의 변이도 반영해야한다. 인터커넥트를 구성하는 금속이 10층 이상 사용되고 있고, 각 층을 구성하는 금속의 저항과 캐패시턴스와 비아 저항이 모두 회로 지연 시간에 영향을 주기 때문에 대표 회로를 찾는 문제는 차원이 매우 높은 영역에서 최적의 해를 찾는 방법이 필요하다. 이를 위해 인터커넥트를 제작하는 공정(백 엔드 오브 라인)의 변이를 반영한 대표 회로를 생성하는 방법을 제안하였다. 공정 변이가 없을때 가장 느린 타이밍 경로에 사용된 게이트와 라우팅 패턴을 변경하면서 점진적으로 탐색하는 방법이다. 구체적으로, 본 논문에서 제안하는 합성 프레임워크는 다음의 새로운 기술들을 통합하였다: (1) 라우팅을 구성하는 여러 금속 층과 비아를 추출하고 탐색 시간 감소를 위해 유사한 구성들을 같은 범주로 분류하였다. (2) 빠르고 정확한 타이밍 분석을 위하여 여러 금속 층과 비아들의 변이를 수식화하였다. (3) 확장성을 고려하여 일반적인 링 오실레이터로 대표회로를 탐색하였다. 두번째로 디자인 룰의 복잡도가 증가하고 있고, 이로 인해 표준 셀들의 인터커넥트를 통한 연결을 진행하는 동안 디자인 룰 위반이 증가하고 있다. 게다가 표준 셀의 크기가 계속 작아지면서 셀들의 연결은 점점 어려워지고 있다. 기존에는 회로 내 모든 표준 셀을 연결하는데 필요한 트랙 수, 가능한 트랙 수, 이들 간의 차이를 이용하여 연결 가능성을 판단하고, 디자인 룰 위반이 발생하지 않도록 셀 배치를 최적화하였다. 그러나 기존 방법은 최신 공정에서는 정확하지 않기 때문에 더 많은 정보를 이용한 회로내 모든 표준 셀 사이의 연결 가능성을 예측하는 방법이 필요하다. 본 논문에서는 기계 학습을 통해 디자인 룰 위반이 발생하는 영역 및 개수를 예측하고 이를 줄이기 위해 표준 셀의 배치를 바꾸는 방법을 제안하였다. 디자인 룰 위반 영역은 이진 분류로 예측하였고 표준 셀의 배치는 디자인 룰 위반 개수를 최소화하는 방향으로 최적화를 수행하였다. 제안하는 프레임워크는 다음의 세가지 기술로 구성되었다: (1) 회로 레이아웃을 여러 개의 정사각형 격자로 나누고 각 격자에서 라우팅을 예측할 수 있는 요소들을 추출한다. (2) 각 격자에서 디자인 룰 위반이 있는지 여부를 판단하는 이진 분류를 수행한다. (3) 메타휴리스틱 최적화 또는 베이지안 최적화를 이용하여 전체 디자인 룰 위반 개수가 감소하도록 각 격자에 있는 표준 셀을 움직인다.Timing analysis and clearing design rule violations are the essential steps for taping out a chip. However, they keep getting harder in deep sub-micron circuits because the variations of transistors and interconnects have been increasing and design rules have become more complex. This dissertation addresses two problems on timing analysis and design rule violations for synthesizing deep sub-micron circuits. Firstly, timing analysis in process corners can not capture post-Si performance accurately because the slowest path in the process corner is not always the slowest one in the post-Si instances. In addition, the proportion of interconnect delay in the critical path on a chip is increasing and becomes over 20% in sub-10nm technologies, which means in order to capture post-Si performance accurately, the representative critical path circuit should reflect not only FEOL (front-end-of-line) but also BEOL (backend-of-line) variations. Since the number of BEOL metal layers exceeds ten and the layers have variation on resistance and capacitance intermixed with resistance variation on vias between them, a very high dimensional design space exploration is necessary to synthesize a representative critical path circuit which is able to provide an accurate performance prediction. To cope with this, I propose a BEOL-aware methodology of synthesizing a representative critical path circuit, which is able to incrementally explore, starting from an initial path circuit on the post-Si target circuit, routing patterns (i.e., BEOL reconfiguring) as well as gate resizing on the path circuit. Precisely, the synthesis framework of critical path circuit integrates a set of novel techniques: (1) extracting and classifying BEOL configurations for lightening design space complexity, (2) formulating BEOL random variables for fast and accurate timing analysis, and (3) exploring alternative (ring oscillator) circuit structures for extending the applicability of this work. Secondly, the complexity of design rules has been increasing and results in more design rule violations during routing. In addition, the size of standard cell keeps decreasing and it makes routing harder. In the conventional P&R flow, the routability of pre-routed layout is predicted by routing congestion obtained from global routing, and then placement is optimized not to cause design rule violations. But it turned out to be inaccurate in advanced technology nodes so that it is necessary to predict routability with more features. I propose a methodology of predicting the hotspots of design rule violations (DRVs) using machine learning with placement related features and the conventional routing congestion, and perturbating placed cells to reduce the number of DRVs. Precisely, the hotspots are predicted by a pre-trained binary classification model and placement perturbation is performed by global optimization methods to minimize the number of DRVs predicted by a pre-trained regression model. To do this, the framework is composed of three techniques: (1) dividing the circuit layout into multiple rectangular grids and extracting features such as pin density, cell density, global routing results (demand, capacity and overflow), and more in the placement phase, (2) predicting if each grid has DRVs using a binary classification model, and (3) perturbating the placed standard cells in the hotspots to minimize the number of DRVs predicted by a regression model.1 Introduction 1 1.1 Representative Critical Path Circuit . . . . . . . . . . . . . . . . . . . 1 1.2 Prediction of Design Rule Violations and Placement Perturbation . . . 5 1.3 Contributions of This Dissertation . . . . . . . . . . . . . . . . . . . 7 2 Methodology for Synthesizing Representative Critical Path Circuits reflecting BEOL Timing Variation 9 2.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Definitions and Overall Flow . . . . . . . . . . . . . . . . . . . . . . 12 2.3 Techniques for BEOL-Aware RCP Generation . . . . . . . . . . . . . 17 2.3.1 Clustering BEOL Configurations . . . . . . . . . . . . . . . . 17 2.3.2 Formulating Statistical BEOL Random Variables . . . . . . . 18 2.3.3 Delay Modeling . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.4 Exploring Ring Oscillator Circuit Structures . . . . . . . . . . 24 2.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5 Further Study on Variations . . . . . . . . . . . . . . . . . . . . . . . 37 3 Methodology for Reducing Routing Failures through Enhanced Prediction on Design Rule Violations in Placement 39 3.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 Overall Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.3 Techniques for Reducing Routing Failures . . . . . . . . . . . . . . . 43 3.3.1 Binary Classification . . . . . . . . . . . . . . . . . . . . . . 43 3.3.2 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.3.3 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.3.4 Placement Perturbation . . . . . . . . . . . . . . . . . . . . . 47 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.4.1 Experiments Setup . . . . . . . . . . . . . . . . . . . . . . . 51 3.4.2 Hotspot Prediction . . . . . . . . . . . . . . . . . . . . . . . 51 3.4.3 Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.4.4 Placement Perturbation . . . . . . . . . . . . . . . . . . . . . 57 4 Conclusions 61 4.1 Synthesis of Representative Critical Path Circuits reflecting BEOL Timing Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.2 Reduction of Routing Failures through Enhanced Prediction on Design Rule Violations in Placement . . . . . . . . . . . . . . . . . . . . . . 62 Abstract (In Korean) 69Docto